Orbax让JAX分布式训练存档更快更省心，性能超PyTorch三倍。
Orbax: Distributed Checkpointing with JAX
- 专为JAX设计的分布式存档库，抽象硬件复杂性
- 存档速度比PyTorch快3.5倍，加载快2倍
- 适合做大规模JAX模型训练的研究者
在高性能分布式机器学习系统中，JAX已成为首选框架。然而，其模块化设计理念使其缺乏标准化的检查点解决方案。本文提出Orbax，一个原生支持JAX的模块化检查点库，能够抽象分布式加速器系统的复杂性，同时在模型生命周期中提供用户友好的检查点操作灵活性。实验表明，其存档性能相比同类PyTorch方案最高提升3.5倍，加载性能提升2倍。该库已开源：https://github.com/google/orbax。
原文摘要 · Abstract (English)
In a landscape of high-performance distributed ML systems, JAX has emerged as a framework of choice. However, JAX's modular design philosophy leaves it without a standardized checkpointing solution. In this paper, we introduce Orbax, a modular, JAX-native checkpointing library that abstracts the complexities of distributed accelerator systems while also providing flexibility for user-friendly checkpoint manipulations throughout the ML model lifecycle. We demonstrate performance exceeding comparable PyTorch competitors by up to 3.5$\times$ for saving and 2$\times$ for loading. The library is available at https://github.com/google/orbax.
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